Computational simulations of anatomical structures and body surface electrode positioning

ABSTRACT

A method may include identifying a simulated three-dimensional representation corresponding to an internal anatomy of a subject based on a match between a computed two-dimensional image corresponding to the simulated three-dimensional representation and a two-dimensional image depicting the internal anatomy of the subject. Simulations of the electrical activities measured by a recording device with standard lead placement and nonstandard lead placement may be computed based on the simulated three-dimensional representation. A clinical electrogram and/or a clinical vectorgram for the subject may be corrected based on a difference between the simulations of electrical activities to account for deviations arising from patient-specific lead placement as well as variations in subject anatomy and pathophysiology.

RELATED APPLICATION

This application claims priority to U.S. Provisional Application No.62/694,401 entitled “COMPUTATIONAL THORACIC AND ECG TRANSFORM VIA 2DRADIOGRAPHY” and filed on Jul. 5, 2018, the disclosure of which isincorporated herein by reference in its entirety.

TECHNICAL FIELD

The subject matter described herein relates generally to medical imagingand more specifically to computationally simulating images of anatomicalstructures and electrical activity to permit the accurate determinationof subject 3-dimensional anatomy and electrical rhythm diagnosis andsource localization.

BACKGROUND

Medical imaging refers to techniques and processes for obtaining datacharacterizing a subject's internal anatomy and pathophysiologyincluding, for example, images created by the detection of radiationeither passing through the body (e.g. x-rays) or emitted by administeredradiopharmaceuticals (e.g. gamma rays from technetium (99mTc) medronicacid given intravenously). By revealing internal anatomical structuresobscured by other tissues such as skin, subcutaneous fat, and bones,medical imagining is integral to numerous medical diagnosis and/ortreatments. Examples of medical imaging modalities include 2-dimensionalimaging such as: x-ray plain films; bone scintigraphy; and thermography,and 3-dimensional imaging modalities such as: magnetic resonance imaging(MRI); computed tomography (CT), cardiac sestamibi scanning, andpositron emission tomography (PET) scanning.

SUMMARY

Systems, methods, and articles of manufacture, including computerprogram products, are provided for computationally simulating athree-dimensional representation of an anatomical structure. In someexample embodiments, there is provided a system that includes at leastone processor and at least one memory. The at least one memory mayinclude program code that provides operations when executed by the atleast one processor. The operations may include: identifying, in alibrary including a plurality of simulated three-dimensionalrepresentations, a first simulated three-dimensional representationcorresponding to a first internal anatomy of a first subject, the firstsimulated three-dimensional representation being identified based atleast on a match between a first computed two-dimensional imagecorresponding to the first simulated three-dimensional representationand a two-dimensional image depicting the first internal anatomy of thefirst subject; and generating an output including the simulatedthree-dimensional representation of the first internal anatomy of thefirst subject.

In some variations, one or more features disclosed herein including thefollowing features can optionally be included in any feasiblecombination. The operations may further include generating the libraryincluding by generating, based on a first three-dimensionalrepresentation of a second internal anatomy of a second subject, thefirst simulated three-dimensional representation. The first simulatedthree-dimensional representation may be generated by at least varyingone or more attributes of the second internal anatomy of the secondsubject. The one or more attributes may include a skeletal property, anorgan geometry, a musculature, and/or a subcutaneous fat distribution.The library may be further generated to include the firstthree-dimensional representation of the second internal anatomy of thesecond subject and/or a second three-dimensional representation of athird internal anatomy of a third subject having at least one differentattribute than the second internal anatomy of the second subject.

In some variations, the generating of the library may includegenerating, based at least on the first simulated three-dimensionalrepresentation, the first computed two-dimensional image. The generatingof the first computed two-dimensional image may include determining,based at least on a density and/or a transmissivity of one or moretissues included in the first simulated three-dimensionalrepresentation, a quantity of radiation able to pass through the one ormore tissues included in the first simulated three-dimensionalrepresentation to form the first computed two-dimensional image.

In some variations, the first three-dimensional representation of thesecond internal anatomy of the second subject may include a computedtomography (CT) scan and/or a magnetic resonance imaging (MRI) scandepicting the second internal anatomy of the second subject.

In some variations, the first simulated three-dimensional representationmay be further associated with a diagnosis of a condition depicted inthe first simulated three-dimensional representation, and wherein theoutput is further generated to include the diagnosis.

In some variations, the operations may further include determining afirst similarity index indicating a closeness of the match between thefirst computed two-dimensional image and the two-dimensional imagedepicting the first internal anatomy of the first subject. The firstsimulated three-dimensional representation may be identified ascorresponding to the first internal anatomy of the first subject basedat least on the first similarity index exceeding a threshold valueand/or the first similarity index being greater than a second similarityindex indicating a closeness of a match between a second computedtwo-dimensional image corresponding to a second simulatedthree-dimensional representation and the two-dimensional image depictingthe first internal anatomy of the first subject.

In some variations, the first computed two-dimensional image may bedetermined to match the two-dimensional image depicting the firstinternal anatomy of the first subject by at least applying an imagecomparison technique. The image comparison technique may include scaleinvariant feature transform (SIFT), speed up robust feature (SURF),binary robust independent elementary features (BRIEF), and/or orientedFAST and rotated BRIEF (ORB).

In some variations, the image comparison technique may include a machinelearning model. The machine learning model may include an autoencoderand/or a neural network.

In some variations, the operations may further include: determining,based at least on the two-dimensional image depicting the first internalanatomy of the first subject, a lead placement for a recording deviceconfigured to measure an electrical activity of an organ, the recordingdevice including one or more leads configured to detect a change involtage on a body surface corresponding to the electrical activity ofthe organ; and generating, based at least on the lead placement and thefirst simulated three-dimensional representation of the first internalanatomy of the first subject, a simulation of the electrical activitymeasured by the recording device.

In some variations, the simulation of the electrical activity measuredby the recording device may include a signal detected by each of the oneor more leads included in the recording device. The recording device maybe configured to perform an electrocardiography (ECG) and/or anelectroencephalography (EEG). The output may be further generated toinclude the lead placement and/or the simulation of the electricalactivity measured by the recording device.

In some variations, the identifying of the first simulatedthree-dimensional representation may further include eliminating asecond simulated three-dimensional representation based at least on amismatch between a demographics and/or a vital statistics of the firstsubject and a second subject depicted in the second simulatedthree-dimensional representation.

In some variations, the identifying of the first simulatedthree-dimensional representation may further include eliminating asecond simulated three-dimensional representation based at least on acondition depicted in the second simulated three-dimensionalrepresentation being inconsistent with one or more symptoms of the firstsubject.

In some variations, the operations may further include providing, to aclient, the output including by sending, to the client, at least aportion of the output and/or generating a user interface configured todisplay at least the portion of the output at the client.

In another aspect, there is provided a method for computationallysimulating a three-dimensional representation of an anatomicalstructure. The method may include: identifying, in a library including aplurality of simulated three-dimensional representations, a firstsimulated three-dimensional representation corresponding to a firstinternal anatomy of a first subject, the first simulatedthree-dimensional representation being identified based at least on amatch between a first computed two-dimensional image corresponding tothe first simulated three-dimensional representation and atwo-dimensional image depicting the first internal anatomy of the firstsubject; and generating an output including the simulatedthree-dimensional representation of the first internal anatomy of thefirst subject.

In some variations, one or more features disclosed herein including thefollowing features can optionally be included in any feasiblecombination. The method may further include generating the libraryincluding by generating, based on a first three-dimensionalrepresentation of a second internal anatomy of a second subject, thefirst simulated three-dimensional representation. The first simulatedthree-dimensional representation may be generated by at least varyingone or more attributes of the second internal anatomy of the secondsubject. The one or more attributes may include a skeletal property, anorgan geometry, a musculature, and/or a subcutaneous fat distribution.The library may be further generated to include the firstthree-dimensional representation of the second internal anatomy of thesecond subject and/or a second three-dimensional representation of athird internal anatomy of a third subject having at least one differentattribute than the second internal anatomy of the second subject.

In some variations, the generating of the library may includegenerating, based at least on the first simulated three-dimensionalrepresentation, the first computed two-dimensional image. The generatingof the first computed two-dimensional image may include determining,based at least on a density and/or a transmissivity of one or moretissues included in the first simulated three-dimensionalrepresentation, a quantity of radiation able to pass through the one ormore tissues included in the first simulated three-dimensionalrepresentation to form the first computed two-dimensional image.

In some variations, the first three-dimensional representation of thesecond internal anatomy of the second subject may include a computedtomography (CT) scan and/or a magnetic resonance imaging (MRI) scandepicting the second internal anatomy of the second subject.

In some variations, the first simulated three-dimensional representationmay be further associated with a diagnosis of a condition depicted inthe first simulated three-dimensional representation, and wherein theoutput is further generated to include the diagnosis.

In some variations, the method may further include determining a firstsimilarity index indicating a closeness of the match between the firstcomputed two-dimensional image and the two-dimensional image depictingthe first internal anatomy of the first subject. The first simulatedthree-dimensional representation may be identified as corresponding tothe first internal anatomy of the first subject based at least on thefirst similarity index exceeding a threshold value and/or the firstsimilarity index being greater than a second similarity index indicatinga closeness of a match between a second computed two-dimensional imagecorresponding to a second simulated three-dimensional representation andthe two-dimensional image depicting the first internal anatomy of thefirst subject.

In some variations, the first computed two-dimensional image may bedetermined to match the two-dimensional image depicting the firstinternal anatomy of the first subject by at least applying an imagecomparison technique. The image comparison technique may include scaleinvariant feature transform (SIFT), speed up robust feature (SURF),binary robust independent elementary features (BRIEF), and/or orientedFAST and rotated BRIEF (ORB).

In some variations, the image comparison technique may include a machinelearning model. The machine learning model may include an autoencoderand/or a neural network.

In some variations, the method may further include: determining, basedat least on the two-dimensional image depicting the first internalanatomy of the first subject, a lead placement for a recording deviceconfigured to measure an electrical activity of an organ, the recordingdevice including one or more leads configured to detect a change involtage on a body surface corresponding to the electrical activity ofthe organ; and generating, based at least on the lead placement and thefirst simulated three-dimensional representation of the first internalanatomy of the first subject, a simulation of the electrical activitymeasured by the recording device.

In some variations, the simulation of the electrical activity measuredby the recording device may include a signal detected by each of the oneor more leads included in the recording device. The recording device maybe configured to perform an electrocardiography (ECG) and/or anelectroencephalography (EEG). The output may be further generated toinclude the lead placement and/or the simulation of the electricalactivity measured by the recording device.

In some variations, the identifying of the first simulatedthree-dimensional representation may further include eliminating asecond simulated three-dimensional representation based at least on amismatch between a demographics and/or a vital statistics of the firstsubject and a second subject depicted in the second simulatedthree-dimensional representation.

In some variations, the identifying of the first simulatedthree-dimensional representation may further include eliminating asecond simulated three-dimensional representation based at least on acondition depicted in the second simulated three-dimensionalrepresentation being inconsistent with one or more symptoms of the firstsubject.

In some variations, the method may further include providing, to aclient, the output including by sending, to the client, at least aportion of the output and/or generating a user interface configured todisplay at least the portion of the output at the client.

In another aspect, there is provided a computer program productincluding a non-transitory computer readable medium storinginstructions. The instructions may cause operations may executed by atleast one data processor. The operations may include: identifying, in alibrary including a plurality of simulated three-dimensionalrepresentations, a first simulated three-dimensional representationcorresponding to a first internal anatomy of a first subject, the firstsimulated three-dimensional representation being identified based atleast on a match between a first computed two-dimensional imagecorresponding to the first simulated three-dimensional representationand a two-dimensional image depicting the first internal anatomy of thefirst subject; and generating an output including the simulatedthree-dimensional representation of the first internal anatomy of thefirst subject.

In another aspect, there is provide an apparatus for computationallysimulating a three-dimensional representation of an anatomicalstructure. The apparatus may include: means for identifying, in alibrary including a plurality of simulated three-dimensionalrepresentations, a first simulated three-dimensional representationcorresponding to a first internal anatomy of a first subject, the firstsimulated three-dimensional representation being identified based atleast on a match between a first computed two-dimensional imagecorresponding to the first simulated three-dimensional representationand a two-dimensional image depicting the first internal anatomy of thefirst subject; and means for generating an output including thesimulated three-dimensional representation of the first internal anatomyof the first subject.

Systems, methods, and articles of manufacture, including computerprogram products, are also provided for computationally correcting aelectrogram. In some example embodiments, there is provided a systemthat includes at least one processor and at least one memory. The atleast one memory may include program code that provides operations whenexecuted by the at least one processor. The operations may include:identifying a three-dimensional representation of at least a portion ofan anatomy of a subject including a target organ; identifying anon-standard lead placement of one or more electrogram leads on a bodyof the subject; generating, based at least on the three-dimensionalrepresentation, one or more simulated electrical activations of thetarget organ; generating, based at least on the one or more simulatedelectrical activations, a non-standard electrogram associated with thenon-standard lead placement of the one or more electrogram leads on thebody of the subject; generating, based at least on the one or moresimulated electrical activations, a standard electrogram associated witha standard lead placement of the one or more electrogram leads on thebody of the subject; and correcting, based at least on a differencebetween the nonstandard electrogram and the standard electrogram, anactual electrogram generated for the subject using the non-standard leadplacement.

In some variations, one or more features disclosed herein including thefollowing features can optionally be included in any feasiblecombination. The standard electrogram, the nonstandard electrogram, andthe actual electrogram may include electrocardiograms,electroencephalograms, or vectorcardiograms.

In some variations, the correcting may include generating atransformation matrix to transform the nonstandard electrogram to thestandard electrogram and applying the transformation matrix to theactual electrogram.

In some variations, the identifying of the three-dimensionalrepresentation may include comparing a two-dimensional image of theportion of the anatomy of the subject to one or more two-dimensionalimages included in a library mapping the one or more two-dimensionalimages to one or more corresponding three-dimensional representations.

In some variations, the nonstandard lead placement may be identifiedbased at least on an analysis of a two-dimensional image of the portionof the anatomy.

In some variations, the operations may further include identifying asimulated electrogram matching the corrected electrogram by at leastsearching a library including a plurality of simulated electrograms. Thelibrary may map the plurality of simulated electrograms to one or morecharacteristics of the target organ used to generate the plurality ofsimulated electrograms.

In another aspect, there is provided a method for computationallycorrecting an electrogram. The method may include: identifying athree-dimensional representation of at least a portion of an anatomy ofa subject including a target organ; identifying a non-standard leadplacement of one or more electrogram leads on a body of the subject;generating, based at least on the three-dimensional representation, oneor more simulated electrical activations of the target organ;generating, based at least on the one or more simulated electricalactivations, a non-standard electrogram associated with the non-standardlead placement of the one or more electrogram leads on the body of thesubject; generating, based at least on the one or more simulatedelectrical activations, a standard electrogram associated with astandard lead placement of the one or more electrogram leads on the bodyof the subject; and correcting, based at least on a difference betweenthe nonstandard electrogram and the standard electrogram, an actualelectrogram generated for the subject using the non-standard leadplacement.

In some variations, one or more features disclosed herein including thefollowing features can optionally be included in any feasiblecombination. The standard electrogram, the nonstandard electrogram, andthe actual electrogram may include electrocardiograms,electroencephalograms, or vectorcardiograms.

In some variations, the correcting may include generating atransformation matrix to transform the nonstandard electrogram to thestandard electrogram and applying the transformation matrix to theactual electrogram.

In some variations, the identifying of the three-dimensionalrepresentation may include comparing a two-dimensional image of theportion of the anatomy of the subject to one or more two-dimensionalimages included in a library mapping the one or more two-dimensionalimages to one or more corresponding three-dimensional representations.

In some variations, the nonstandard lead placement may be identifiedbased at least on an analysis of a two-dimensional image of the portionof the anatomy.

In some variations, the method may further include identifying asimulated electrogram matching the corrected electrogram by at leastsearching a library including a plurality of simulated electrograms. Thelibrary may map the plurality of simulated electrograms to one or morecharacteristics of the target organ used to generate the plurality ofsimulated electrograms.

In another aspect, there is provided a computer program productincluding a non-transitory computer readable medium storinginstructions. The instructions may cause operations may executed by atleast one data processor. The operations may include: identifying athree-dimensional representation of at least a portion of an anatomy ofa subject including a target organ; identifying a non-standard leadplacement of one or more electrogram leads on a body of the subject;generating, based at least on the three-dimensional representation, oneor more simulated electrical activations of the target organ;generating, based at least on the one or more simulated electricalactivations, a non-standard electrogram associated with the non-standardlead placement of the one or more electrogram leads on the body of thesubject; generating, based at least on the one or more simulatedelectrical activations, a standard electrogram associated with astandard lead placement of the one or more electrogram leads on the bodyof the subject; and correcting, based at least on a difference betweenthe nonstandard electrogram and the standard electrogram, an actualelectrogram generated for the subject using the non-standard leadplacement.

In another aspect, there is provided an apparatus for computationallycorrecting an electrogram. The apparatus may include: means foridentifying a three-dimensional representation of at least a portion ofan anatomy of a subject including a target organ; means for identifyinga non-standard lead placement of one or more electrogram leads on a bodyof the subject; means for generating, based at least on thethree-dimensional representation, one or more simulated electricalactivations of the target organ; means for generating, based at least onthe one or more simulated electrical activations, a non-standardelectrogram associated with the non-standard lead placement of the oneor more electrogram leads on the body of the subject; means forgenerating, based at least on the one or more simulated electricalactivations, a standard electrogram associated with a standard leadplacement of the one or more electrogram leads on the body of thesubject; and means for correcting, based at least on a differencebetween the nonstandard electrogram and the standard electrogram, anactual electrogram generated for the subject using the non-standard leadplacement.

Implementations of the current subject matter can include systems andmethods consistent including one or more features are described as wellas articles that comprise a tangibly embodied machine-readable mediumoperable to cause one or more machines (e.g., computers, etc.) to resultin operations described herein. Similarly, computer systems are alsodescribed that may include one or more processors and one or morememories coupled to the one or more processors. A memory, which caninclude a computer-readable storage medium, may include, encode, store,or the like one or more programs that cause one or more processors toperform one or more of the operations described herein. Computerimplemented methods consistent with one or more implementations of thecurrent subject matter can be implemented by one or more data processorsresiding in a single computing system or multiple computing systems.Such multiple computing systems can be connected and can exchange dataand/or commands or other instructions or the like via one or moreconnection including, for example, a connection over a network (e.g. theInternet, a wireless wide area network, a local area network, a widearea network, a wired network, or the like), a direct connection betweenone or more of the multiple computing systems, and/or the like.

The details of one or more variations of the subject matter describedherein are set forth in the accompanying drawings and the descriptionbelow. Other features and advantages of the subject matter describedherein may be apparent from the description and drawings, and from theclaims. While certain features of the currently disclosed subject matterare described for illustrative purposes in relation to computationallysimulating images of anatomical structures, it should be readilyunderstood that such features are not intended to be limiting. Theclaims that follow this disclosure are intended to define the scope ofthe protected subject matter.

DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this specification, show certain aspects of the subject matterdisclosed herein and, together with the description, help explain someof the principles associated with the disclosed implementations. In thedrawings,

FIG. 1 depicts a system diagram illustrating an imaging system, inaccordance with some example embodiments;

FIG. 2 depicts a block diagram illustrating a block diagram illustratingan example of identifying a simulated three-dimensional representationwhich most closely corresponds to a subject's internal anatomy, inaccordance with some example embodiments;

FIG. 3A depicts an example of a simulated three-dimensionalrepresentation of a skeletal anatomy of a reference subject, inaccordance with some example embodiments;

FIG. 3B depicts another example of a simulated three-dimensionalrepresentation of a skeletal anatomy of a reference subject, inaccordance with some example embodiments;

FIG. 3C depicts another example of a simulated three-dimensionalrepresentation of a skeletal anatomy of a reference subject, inaccordance with some example embodiments;

FIG. 4A depicts an example of a simulated three-dimensionalrepresentation of a cardiac anatomy of a reference subject, inaccordance with some example embodiments;

FIG. 4B depicts another example of a simulated three-dimensionalrepresentation of a cardiac anatomy of a reference subject, inaccordance with some example embodiments;

FIG. 4C depicts another example of a simulated three-dimensionalrepresentation of a cardiac anatomy of a reference subject, inaccordance with some example embodiments;

FIG. 5 depicts an example of a technique for generating a computedtwo-dimensional image, in accordance with some example embodiments;

FIG. 6A depicts an example of a clinical, two-dimensionalanteroposterior (AP) chest x-ray image showing subject anatomy, thepresence of an implantable cardioverter-defibrillator, and the positionsof body surface electrodes, in accordance with some example embodiments;

FIG. 6B depicts an example of a clinical, two-dimensional lateral chestx-ray image showing subject anatomy, the presence of an implantablecardioverter-defibrillator, and the positions of body surfaceelectrodes, in accordance with some example embodiments;

FIG. 7 depicts an example of the standard positioning of body surfaceelectrodes (e.g the precordial leads for a 12-lead electrocardiogram)for measuring the electrical activities of an organ (e.g. the heart), inaccordance with some example embodiments;

FIG. 8 depicts an example of an output from a recording device measuringthe electrical activities of an organ (e.g. a standard 12-leadelectrocardiogram), in accordance with some example embodiments;

FIG. 9A depicts a flowchart illustrating an example of an imagingprocess, in accordance with some example embodiments;

FIG. 9B depicts a flowchart illustrating an example of an imagingprocess and generation of a computational model of a subject, inaccordance with some example embodiments;

FIG. 9C depicts a diagram illustrating an example of process forgenerating a corrected electrogram, in accordance with some exampleembodiments;

FIG. 9D depicts a diagram illustrating an example of process forgenerating a corrected vectorgram, in accordance with some exampleembodiments; and

FIG. 10 depicts a block diagram illustrating a computing system, inaccordance with some example embodiments.

When practical, similar reference numbers denote similar structures,features, or elements.

DETAILED DESCRIPTION

Although widely available and less expensive, projectional, or2-dimensional, radiography techniques (e.g., X-ray plain films, gammaray imaging (e.g. bone scintigraphy), fluoroscopy, and/or the like) areonly able to generate two-dimensional images of a subject's internalanatomy, which may be inadequate for a variety of medical diagnosis andtreatments. Conventional techniques for generating a three-dimensionalrepresentation of a subject's internal anatomy include computedtomography (CT) and magnetic resonance imaging (MRI). However, computedtomography and magnetic resonance imaging requires specializedequipment, trained technicians, often involves more time to obtain, andmay be difficult to perform during invasive procedures or on criticallyill subjects. As such, computed tomography and magnetic resonanceimaging tend to be less accessible, more cost prohibitive, and ofteninfeasible compared with projectional radiographs.

In some example embodiments, instead of relying on computed tomographyor magnetic resonance imaging to obtain a three-dimensionalrepresentation of subject's internal anatomy, a simulatedthree-dimensional representation of a subject's internal anatomy may bedetermined based on one or more two-dimensional images of the subject'sinternal anatomy. For example, a simulated three-dimensionalrepresentation corresponding to the subject's internal anatomy may beidentified based on one or more two-dimensional images of the subject'sinternal anatomy (e.g. FIGS. 6A and 6B). The two-dimensional images ofthe subject's internal anatomy may be obtained using a projectionalradiography technique including, for example, X-rays, gamma ray imaging(e.g. bone scintigraphy), fluoroscopy, and/or the like. Meanwhile, thesimulated three-dimensional representation may be part of a library ofsimulated three-dimensional representations, each of which beingassociated with one or more corresponding two-dimensional images. Forinstance, one or more simulated radiograph images (e.g., X-ray images,gamma ray images, and/or the like) may be generated based on each of thesimulated three-dimensional representations included in the library.Accordingly, identifying the simulated three-dimensional representationcorresponding to the subject's internal anatomy may include matching thetwo-dimensional images of the subject's internal anatomy to the computedtwo-dimensional images associated with the simulated three-dimensionalrepresentation.

The library of simulated three-dimensional representations includes oneor more existing three-dimensional representations of the internalanatomies of one or more reference subjects including, for example,computed tomography scans, magnetic resonance imaging scans, and/or thelike. The reference subjects may exhibit a variety of differentanatomical attributes including, for example, variations in skeletalproperties (e.g., size, abnormalities, and/or the like), organ geometry(e.g., size, relative position, and/or the like), musculature,subcutaneous fat distribution, and/or the like. As such, the simulatedthree-dimensional representations included in the library may alsodepict a variety of different anatomical attributes. Furthermore,additional anatomical variations may be introduced into the library ofsimulated three-dimensional representations by at least generating,based on the existing three-dimensional representations, one or moresimulated three-dimensional representations that include at leastvariation to the internal anatomy of the corresponding referencesubject. For example, in one representation, a muscle (e.g. thepectoralis major muscle) may be 5 mm in thickness. In anotherrepresentation, the muscle (e.g. the pectoralis major muscle) may be 10mm in thickness. For instance, based on an existing three-dimensionalrepresentation of the internal anatomy of a reference subject, one ormore additional simulated three-dimensional representations may begenerated to include variations in the skeletal properties (e.g., size,abnormalities, and/or the like), organ geometries (e.g., size, relativeposition, and/or the like), musculature, and/or subcutaneous fatdistribution of the same reference subject.

Each simulated three-dimensional representation included in the librarymay be associated with one or more computed two-dimensional imagesincluding, for example, X-ray images, gamma ray images, and/or the like.A computed two-dimensional image may be generated based at least oneither (a) a density and/or radiation transmissivity of the differenttissues forming each of the anatomical structures (e.g., organs)included in a corresponding simulated three-dimensional representation,or (b) the absorption rate of radiopharmaceuticals (e.g. technetium(99mTc) medronic acid and/or the like) by different tissues and theemission rate of the radiopharmaceutical. Moreover, multiple computedtwo-dimensional image may be generated for each simulatedthree-dimensional representation in order to capture different views ofthe simulated three-dimensional representation including, for example, aleft anterior oblique view, a right anterior oblique view, a straightanterior-posterior view, and/or the like. For example, a simulated X-rayimage of the simulated three-dimensional representation of a human torsomay be generated based at least in part on the respective of densityand/or radiation transmissivity of the various anatomical structuresincluded in the human torso such as skin, bones, subcutaneous fat,visceral fat, heart, lungs, liver, stomach, intestines, and/or the like.In some variations, this may be accomplished using the software platformBlender (Blender Foundation, Amsterdam, Netherlands). In somevariations, a 3-dimensional model of the body may be loaded intoBlender. Different tissues within the model may be assigned differentlight transmissivities (e.g. greater transmissivity for subcutaneousfat, less transmissivity for bone). A simulated light source may beplaced on one side of the model, and a flat surface placed on the otherside of the model. The transmission of light through the model iscomputed, and an image of the projection on the two dimensional surfaceis recorded. This image may be further manipulated (e.g. white-blackinversion) to produce a simulated 2-dimensional radiograph. As noted, insome example embodiments, the simulated three-dimensional representationcorresponding to the subject's internal anatomy may be identified byleast matching the two-dimensional images of the subject's internalanatomy to computed two-dimensional images associated with the simulatedthree-dimensional representation.

In some example embodiments, each of the simulated three-dimensionalrepresentation and the corresponding computed two-dimensional imagesincluded in the library may be associated with a diagnosis. As such,when the two-dimensional images (e.g., X-ray images, gamma ray images,and/or the like) of the subject is matched to computed two-dimensionalimages associated with a three-dimensional representation included inthe library, a diagnosis for the subject may be determined based on thediagnosis that is associated with the computed two-dimensional images.For example, the subject may be determined to have dilatedcardiomyopathy if the two-dimensional images of the subject is matchedto the computed two-dimensional images associated with dilatedcardiomyopathy. It should be appreciated that a two-dimensional image ofthe subject may be matched to one or more computed two-dimensionalimages by applying a variety of image comparison techniques including,for example, scale invariant feature transform (SIFT), speed up robustfeature (SURF), binary robust independent elementary features (BRIEF),oriented FAST and rotated BRIEF (ORB), and/or the like. A match betweena two-dimensional image of the subject and one or more computedtwo-dimensional images may further be determined by applying one or moremachine learning-based image comparison techniques including, forexample, autoencoders, neural networks, and/or the like.

For example, the match between the two-dimensional image and the one ormore computed two-dimensional images may be determined by applying oneor more convolutional neural networks, recurrent neural networks, and/orthe like. The neural network may be trained based on training data thatincludes pairs of matching and/or non-matching two-dimensional images.Moreover, the neural network may be trained to examine features presentin corresponding portions of the two-dimensional image of the subjectand at least some of the computed two-dimensional images included in thelibrary to determine a similarity metric between each pair oftwo-dimensional images.

In some example embodiments, the match between a two-dimensional imageof the subject's internal anatomy and one or more computedtwo-dimensional images may be probabilistic. For example, when atwo-dimensional image of the subject is matched to computedtwo-dimensional images, each of the computed two-dimensional images maybe associated with a value (e.g., a similarity index and/or the like)indicating a closeness of the match between the two-dimensional imageand the computed two-dimensional image. Moreover, multiple diagnosis,including a likelihood for each of the diagnosis, may be determined forthe subject based on the diagnosis associated with each of the computedtwo-dimensional images. For instance, the diagnosis for the subject mayinclude a first probability (e.g., an x-percentage likelihood) of thesubject having dilated cardiomyopathy and a second probability (e.g., anx-percentage likelihood) of the subject having a pulmonary embolismbased at least on the probabilistic match between the two-dimensionalimages of the subject and the computed two-dimensional images includedin the library.

The electrical activities of an organ are typically measured byrecording device having one more leads (e.g., pairs of electrodesmeasuring voltage changes), which may be placed on a surface of the bodynear the organ as in the case of electrocardiography (ECG) for measuringthe electrical activities of the heart and electroencephalography (EEG)for measuring the electrical activities of the brain. Although a commondiagnostic modality in medicine, surface recordings are associated witha number of limitations. For example, surface recordings (e.g.,electrocardiography, electroencephalography, and/or the like) areperformed under the assumption of a standard surface electrogram setup(e.g., lead placement) even though variations in actual lead positioncan alter the morphology of the resulting electrogram and/or vectorgram(e.g., electrocardiogram, electroencephalogram, vectorcardiogram, and/orthe like). The morphology of the resulting electrogram can also bealtered due to significant variations in individual anatomy (e.g.obesity and/or the like) and/or the presence of co-morbidities (e.g. thelung disease emphysema and/or the like), which vary the conduction ofelectrical signals through the body. These electrical alterations canintroduce error into the diagnoses made based on the electrogram as wellas the processes utilizing the electrical signals to map the organ'selectrical activity (e.g. mapping the source of a cardiac arrhythmiaand/or the like). As such, in some example embodiments, asubject-specific computational simulation environment that capturesindividual variations in body surface lead placement and subject anatomymay enable a more accurate calculation of the electrical activity of theorgan (e.g. heart, brain, and/or the like). For instance, a customizedcomputational simulation environment for a subject may be generated toinclude a three-dimensional representation of the internal anatomy (e.g.thoracic anatomy including the heart for measuring cardiac electricalactivity) as described above. The electrical activities of an organ maybe simulated based on the three-dimensional representation of thesubject's internal anatomy. The simulated electrical activities mayinclude normal electrical activations (e.g. sinus rhythm for the heart)as well as abnormal electrical activations (e.g. ventriculartachycardia). Moreover, one or more electrical properties of the organmay be determined based on the simulation of the electrical activitiesof the organ.

In some example embodiments, the placement of each lead of a recordingdevice may be determined based on one or more two-dimensional images ofthe subject's internal anatomy. Based on the simulated electricalactivities of the organ and the known locations for the leads on thesurface of the subject's body, an output for the simulated recordingdevice (e.g., the electrical signals that are detected at eachelectrogram lead) may be determined based on the corresponding simulatedthree-dimensional representation of the subject's internal anatomy togenerate a simulated electrogram (e.g. a simulated electrocardiogram, asimulated electroencephalogram, and/or the like). Once the relationshipbetween the simulated organ (e.g. heart) and simulated electrogramproperties (e.g. nonstandard electrocardiogram lead positions) isdetermined, the relationship between each lead and the likely electricalactivation pattern of the organ can be more accurately calculated. Forexample, the relationship between the simulated organ and the simulatedelectrogram properties may enable the generation of a subject-specifictransformation matrix, or correction matrix, that accounts forvariations in lead placement and subject anatomy. In some embodiments,the accuracy of the simulation algorithm applied to generate thesimulated output may be improved by at least updating the simulationalgorithm based on clinical data including actual measurements of theelectrical activities of the subject's organ as measured from the bodysurface electrodes.

FIG. 1 depicts a system diagram illustrating an imaging system 100, inaccordance with some example embodiments. Referring to FIG. 1 , theimaging system 100 may include a simulation controller 110, a client120, and a data store 130 storing an image library 135. As shown in FIG.1 , the simulation controller 110, the client 120, and the data store130 may be communicatively coupled via a network 140. The network 140may be a wired and/or wireless network including, for example, a widearea network (WAN), a local area network (LAN), a virtual local areanetwork (VLAN), a public land mobile network (PLMN), the Internet,and/or the like. Meanwhile, the data store 130 may be a databaseincluding, for example, a graph database, an in-memory database, arelational database, a non-SQL (NoSQL) database, and/or the like.

In some example embodiments, the simulation controller 110 may beconfigured to identify, based at least on one or more two-dimensionalimages of the subject's internal anatomy, a simulated three-dimensionalrepresentation in the image library 135 that corresponds to thesubject's internal anatomy. For example, the simulation controller 110may receive, from the client 120, on or more two-dimensional images ofthe subject's internal anatomy, which may be generated using aprojectional radiography technique including, for example, X-rays, gammarays, fluoroscopy, thermography, and/or the like. The simulationcontroller 110 may identify the simulated three-dimensionalrepresentation as corresponding to the subject's internal anatomy basedat least on the two-dimensional images of the subject's internal anatomybeing matched with the computed two-dimensional images associated withthe simulated three-dimensional representation.

To further illustrate, FIG. 2 depicts a block diagram illustrating anexample of identifying a simulated three-dimensional representationcorresponding to a subject's internal anatomy, in accordance with someexample embodiments. Referring to FIGS. 1-2 , the simulation controller110 may receive, from the client 120, one or more two-dimensional imagesdepicting an internal anatomy of a subject 210 including, for example, atwo-dimensional image 215. The two-dimensional image 215 may begenerated using a projectional radiography technique including, forexample, X-rays, gamma rays, fluoroscopy, and/or the like. In someexample embodiments, the simulation controller 110 may identify, basedat least on the two-dimensional image 215, one or more simulatedthree-dimensional representations in the image library 135 thatcorresponds to the internal anatomy of the subject 210.

Referring again to FIG. 2 , the image library 135 may include aplurality of simulated three-dimensional representations including, forexample, a first simulated three-dimensional representation 220 a, asecond simulated three-dimensional representation 220 b, a thirdsimulated three-dimensional representation 220 c, and/or the like. Asshown in FIG. 2 , each simulated three-dimensional representationincluded in the image library 135 may be associated with one or morecomputed two-dimensional images, each of which being generated based ona corresponding simulated three-dimensional representation. For example,FIG. 2 shows the first simulated three-dimensional representation 220 abeing associated with a first computed two-dimensional image 225 agenerated based on the first simulated three-dimensional representation220 a, the second simulated three-dimensional representation 220 b beingassociated with a second computed two-dimensional image 225 b generatedbased on the second simulated three-dimensional representation 220 b,and the third simulated three-dimensional representation 220 c beingassociated with a third computed two-dimensional image 225 c generatedbased on the third simulated three-dimensional representation 220 c.

The simulation controller 110 may apply one or more image comparisontechniques in order to determine whether the two-dimensional image 215matches the first computed two-dimensional image 225 a associated withthe first simulated three-dimensional representation 220 a, the secondcomputed two-dimensional image 225 b associated with the secondsimulated three-dimensional representation 220 b, and/or the thirdcomputed two-dimensional image 225 c associated with the third simulatedthree-dimensional representation 220 c. The one or more image comparisontechniques may include scale invariant feature transform (SIFT), speedup robust feature (SURF), binary robust independent elementary features(BRIEF), oriented FAST and rotated BRIEF (ORB), and/or the like.Alternatively and/or additionally, the one or more image comparisontechniques may include one or more machine learning models trained toidentify similar images including, for example, autoencoders, neuralnetworks, and/or the like.

In some example embodiments, the simulation controller 110 may apply theone or more image comparison techniques to generate a probabilisticmatch between the two-dimensional image 215 and one or more of the firstcomputed two-dimensional image 225 a, the second computedtwo-dimensional image 225 b, and the third computed two-dimensionalimage 225 c. As shown in FIG. 2 , each of the first computedtwo-dimensional image 225 a, the second computed two-dimensional image225 b, and the third computed two-dimensional image 225 c may be asimilarity index and/or another value indicating a closeness of thematch to the two-dimensional image 215. For example, the simulationcontroller 110 may determine that the first computed two-dimensionalimage 225 a is 75% similar to the two-dimensional image 215, the secondcomputed two-dimensional image 225 b is 5% similar to thetwo-dimensional image 215, and the third computed two-dimensional image225 c is 55% similar to the two-dimensional image 215. The simulationcontroller 110 may determine, based at least on the respectivesimilarity index, that one or more of the first computed two-dimensionalimage 225 a, the second computed two-dimensional image 225 b, and thethird computed two-dimensional image 225 c match the two-dimensionalimage 215. For instance, the simulation controller 110 may determinethat the first computed two-dimensional image 225 a matches thetwo-dimensional image 215 based on the first computed two-dimensionalimage 225 a being associated with a highest similarity index and/or thefirst computed two-dimensional image 225 a being associated with asimilarity index exceeding a threshold value.

In some example embodiments, the simulation controller 110 may identify,based at least on the computed two-dimensional images matched to thetwo-dimensional image 215, one or more simulated three-dimensionalrepresentations corresponding to the internal anatomy of the subject210. For example, based on the first computed two-dimensional image 225a being determined to match the two-dimensional image 215, thesimulation controller 110 may identify the first simulatedthree-dimensional representation 220 a as corresponding to the internalanatomy of the subject 210.

Furthermore, as shown in FIG. 2 , each of the first simulatedthree-dimensional representation 220 a, the second simulatedthree-dimensional representation 220 b, and the third simulatedthree-dimensional representation 220 c may be associated with adiagnosis. As such, the simulation controller 110 may further determineone or more diagnosis for the subject 210 based at least on the one ormore simulated three-dimensional representations determined tocorrespond to the internal anatomy of the subject 210. When thesimulation controller 110 determines multiple diagnosis for the subject210, each diagnosis may be associated with a probability correspondingto the similarity index between the two-dimensional image 215 and thecomputed two-dimensional image matched with the two-dimensional image215. For example, based on the 75% similarity between thetwo-dimensional image 215 and the first computed two-dimensional image225 a, the simulation controller 110 may determine that there is a 75%chance of the subject 210 being afflicted with dilated cardiomyopathy.Alternatively and/or additionally, based on the 5% similarity betweenthe two-dimensional image 215 and the second computed two-dimensionalimage 225 b, the simulation controller 110 may determine that there is a5% chance of the subject 210 being afflicted with a pulmonary embolism.

In some example embodiments, an actual diagnosis for the subject 210 maybe used to at least refine one or more machine learning-based imagecomparison techniques for matching the two-dimensional image 215 to oneor more of the first computed two-dimensional image 225 a, the secondcomputed two-dimensional image 225 b, and the third computedtwo-dimensional image 225 c. For instance, if the simulation controller110 applying a trained machine learning model (e.g., autoencoder, neuralnetwork, and/or the like) determines that the two-dimensional image 215is matched to the first computed two-dimensional image 225 acorresponding to dilated cardiomyopathy but the actual diagnosis for thesubject 210 is a rib fracture, the simulation controller 110 may atleast retrain the machine learning model to correctly match thetwo-dimensional image 215 to the third computed two-dimensional image225 c. The machine learning model may be retrained based on additionaltraining data that include at least some two-dimensional images thatdepict a rib fracture. The retraining of the machine learning model mayinclude further updating the one or more weights and/or biases appliedby the machine learning model to reduce an error in an output of themachine learning model including, for example, the mismatching oftwo-dimensional images depicting rib fractures.

In order to reduce the time and computation resources associated withsearching the image library 135 for one or more computed two-dimensionalimages matching the two-dimensional image 215, the simulation controller110 may apply one or more filters to eliminate at least some of thecomputed two-dimensional images from the search. For example, thecomputed two-dimensional images (and the corresponding simulatedthree-dimensional representations) included in the image library 135 maybe indexed based on one or more attributes such as, for example, thedemographics (e.g., age, gender, and/or the like) and/or the vitalstatistics (e.g., height, weight, and/or the like) of reference subjectsdepicted in the computed two-dimensional image. Alternatively and/oradditionally, the computed two-dimensional images (and the correspondingsimulated three-dimensional representations) included in the imagelibrary 135 may be indexed based on the corresponding primary symptomand/or complaint of the subject. For example, the first computedtwo-dimensional image 225 a, the second computed two-dimensional image225 b, and the third computed two-dimensional image 225 c may be indexedbased on the complaint or symptom of “chest discomfort.” Alternativelyand/or additionally, the computed two-dimensional images (and thecorresponding simulated three-dimensional representations) included inthe image library 135 may be indexed based on the correspondingdiagnosis and/or types of diagnosis. For instance, the first computedtwo-dimensional image 225 a and the second computed two-dimensionalimage 225 b may be indexed as “heart conditions” while the thirdcomputed two-dimensional image 225 c may be indexed as “bone fractures.”

Accordingly, instead of comparing the two-dimensional image 215 to everycomputed two-dimensional image included in the image library 135, thesimulation controller 110 may eliminate, based on the demographicsand/or the vital statistics of the subject 210, one or more computedtwo-dimensional images of reference subjects having differentdemographics and/or vital statistics than the subject 210. Alternativelyand/or additionally, the simulation controller 110 may furthereliminate, based on one or more symptoms of the subject 210, one or morecomputed two-dimensional images associated with diagnosis that areinconsistent with the symptoms of the subject 210.

Referring again to FIG. 2 , the image library 135 may include aplurality of simulated three-dimensional representations including, forexample, the first simulated three-dimensional representation 220 a, thesecond simulated three-dimensional representation 220 b, the thirdsimulated three-dimensional representation 220 c, and/or the like. Insome example embodiments, the first simulated three-dimensionalrepresentation 220 a, the second simulated three-dimensionalrepresentation 220 b, and/or the third simulated three-dimensionalrepresentation 220 c may be existing three-dimensional representationsof the internal anatomies of one or more reference subjects including,for example, computed tomography scans, magnetic resonance imagingscans, and/or the like. The reference subjects may exhibit a variety ofdifferent anatomical attributes including, for example, variations inskeletal properties (e.g., size, abnormalities, and/or the like), organgeometry (e.g., size, relative position, and/or the like), musculature,subcutaneous fat distribution, and/or the like. As such, the firstsimulated three-dimensional representation 220 a, the second simulatedthree-dimensional representation 220 b, and/or the third simulatedthree-dimensional representation 220 c may also depict a variety ofdifferent anatomical attributes.

According to some example embodiments, additional anatomical variationsmay be introduced computationally into the image library 135 by at leastgenerating, based on the existing three-dimensional representations, oneor more simulated three-dimensional representations that include atleast variation to the internal anatomy of the corresponding referencesubject. For instance, the first simulated three-dimensionalrepresentation 220 a, the second simulated three-dimensionalrepresentation 220 b, and/or the third simulated three-dimensionalrepresentation 220 c may be generated, based on one or more existingthree-dimensional representations of the internal anatomy of a referencesubject, to include variations in the skeletal properties (e.g., size,abnormalities, and/or the like), organ geometries (e.g., size, relativeposition, and/or the like), musculature, and/or subcutaneous fatdistribution of the same reference subject.

To further illustrate, FIGS. 3A-C and 4A-C depicts examples of simulatedthree-dimensional representations of internal anatomies, in accordancewith some example embodiments. FIGS. 3A-C and 4A-C depict examples ofsimulated three-dimensional representations that may be generated basedon existing three-dimensional representations of the internal anatomiesof one or more reference subjects including, for example, computedtomography scans, magnetic resonance imaging scans, and/or the like.Furthermore, FIGS. 3A-C and 4A-C depict examples of simulatedthree-dimensional representations with computationally introducedanatomical variations including, for example, variations in skeletalproperties (e.g., size, abnormalities, and/or the like), organgeometries (e.g., size, relative position, and/or the like),musculature, subcutaneous fat distribution, and/or the like.

For example, FIG. 3A-C depict examples of simulated three-dimensionalrepresentations of skeletal anatomy, in accordance with some exampleembodiments. FIG. 3A may depict a simulated three-dimensionalrepresentation 310 of the skeletal anatomy of a first reference subjectwho is a 65 years old, male, 6 feet 5 inches tall, weighing 220 pounds,and having severe congestive heart failure with a left ventricularejection fraction of 25%. FIG. 3B may depict a simulatedthree-dimensional representation 320 of the skeletal anatomy of a secondreference subject who is 70 years old, female, 5 feet 7 inches tall,weighing 140 pounds, and having moderate chronic systolic congestiveheart failure with a left ventricular ejection fraction of 35%.Furthermore, FIG. 3C may depict a simulated three-dimensionalrepresentation 330 of the skeletal anatomy of a third reference subjectwho is 18 years old, weighing 120 pounds, and having a congenital heartdisease with an ejection fraction of 45%. As noted, FIGS. 3A-C may beindexed based on one or more attributes including, for example, thedemographics (e.g., age, gender, and/or the like), the vital statistics(e.g., weight, height, and/or the like), and/or the condition of thecorresponding reference subject.

FIGS. 4A-C depicts examples of simulated three-dimensionalrepresentations of cardiac anatomies, in accordance with some exampleembodiments. FIG. 4A depicts a simulated three-dimensionalrepresentation 410 of a heart with moderate congestive heart failure, anejection fraction of 40%, and a ventricular axis of 30 degrees (shown asa black line) in the frontal plane. FIG. 4B depicts a simulatedthree-dimensional representation 420 of a heart with a normal ejectionfraction of 57% and a ventricular axis of 45 degrees (shown as a blackline) in the frontal plane. Furthermore, FIG. 4C depicts a simulatedthree-dimensional representation 420 of a heart with severe leftventricular dysfunction, an ejection fraction of 20%, and a ventricularaxis of 20 degrees (shown as a black line) in the frontal plane. FIGS.4A-C may also be indexed based on one or more attributes including, forexample, the demographics (e.g., age, gender, and/or the like), thevital statistics (e.g., weight, height, and/or the like), and/or thecondition of the corresponding reference subject.

As noted, the simulated three-dimensional representations included inthe image library 135 may be used to generate the computedtwo-dimensional images included in the image library 135. For example,referring again to FIG. 2 , the first computed two-dimensional image 225a may be generated based on the first simulated three-dimensionalrepresentation 220 a, the second computed two-dimensional image 225 bmay be generated based on the second simulated three-dimensionalrepresentation 220 b, and the third computed two-dimensional image 225 cmay be generated based on the third simulated three-dimensionalrepresentation 220 c.

The computed two-dimensional images included in the image library 135may correspond to radiograph images (e.g., X-ray images, gamma rayimages, fluoroscopy images, and/or the like), which are typicallycaptured using a projectional, or 2-dimensional radiography techniques,in which at least a portion of a subject is exposed to electromagneticradiation (e.g., X-rays, gamma rays, and/or the like). As such, in someexample embodiments, a computed two-dimensional image may be generatedby at least simulating the effects of being exposed to a radiationsource. For example, the computed two-dimensional image based at leaston a density and/or radiation transmissivity of the different tissuesincluded in the simulated three-dimensional representation.

To further illustrate, FIG. 5 depicts an example of a technique forgenerating a computed two-dimensional image, in accordance with someexample embodiments. Referring to FIG. 5 , a computed two-dimensionalimage 510 may be generated (e.g. using the software Blender (BlenderFoundation, Amsterdam, Netherlands)) by at least simulating the effectsof exposing, to a simulated radiation source 520 (e.g. light), asimulated three-dimensional representation 530 of an internal anatomy(e.g., a thoracic cavity and/or the like). The computed two-dimensionalimage 510 may be generated by at least determining, based at least on adensity and/or transmissivity of the different tissues included in thesimulated three-dimensional representation 530, a quantity of simulatedradiation (e.g., from the simulated radiation source 520) that is ableto pass through the different tissues included in the simulatedthree-dimensional representation 530 onto a simulated surface. An imageof this project is then recorded and further processed (e.g. white-blackinversion) to form the computed two-dimensional image 510.

In some example embodiments, a view of the simulated three-dimensionalrepresentation 530 (e.g., straight anterior-posterior, anterior oblique,and/or the like) that is captured in the computed two-dimensional image510 may be varied by at least varying a position and/or an orientationof the simulated radiation source 520 relative of the simulatedthree-dimensional representation 530. Accordingly, multiple computedtwo-dimensional image may be generated for each simulatedthree-dimensional representation in order to capture different views ofthe simulated three-dimensional representation including, for example, aleft anterior oblique view, a right anterior oblique view, a straightanterior-posterior view, and/or the like.

As noted, the electrical activities of an organ (e.g., heart, brain,and/or the like) is typically measured by a recording device one or morebody surface leads, which may be surface electrodes configured tomeasure voltage changes on the surface of the subject's skincorresponding to the electrical activities of the organ. For example,FIG. 6A depicts an example of a clinical two-dimensional image 610showing a posterior-anterior (PA) view. Notably, FIG. 6A depicts thepositions of a number of surface electrodes including, for example, afirst surface electrode 615 a, a second surface electrode 615 b, and athird surface electrode 615 c. It should be appreciated that one or moreof the first surface electrode 615 a, the second surface electrode 615b, and the third surface electrode 615 c may be in a non-standardpositions. FIG. 6B depicts another example of a clinical two-dimensionalimage 620 showing a left lateral view of the same subject. Again, thepositions of several surface electrodes may also be observed in theclinical two-dimensional image 620.

Additionally, FIG. 7 depicts an example of leads for measuring theelectrical activities of the heart. As shown in FIG. 7 , a plurality ofleads (e.g., V1, V2, V3, V4, V5, and V6) may be placed on the surface ofthe subject's skin. Each of the plurality of leads may be configured tomeasure a voltage change on the surface of the subject's skin thatcorresponds to the electrical activities of the subject's heartincluding, for example, the dipole that is created due to the successivedepolarization and repolarization of the heart. The signal from eachlead may be recorded, in combination with one or more other leads, togenerate, for example, the electrocardiogram 800 shown in FIG. 8 ,demonstrating normal sinus rhythm.

In some example embodiments, the simulation controller 110 may befurther configured to simulate, based on a computed two-dimensionalimage and/or a simulated three-dimensional representation correspondingto a subject's internal anatomy, the electrical activities of an organ(e.g., heart, brain, gastrointestinal system, and/or the like). Afterdetermining the placement of each lead in a simulated recording devicebased on a computed two-dimensional image of the subject's internalanatomy as described previously, the output for the simulated recordingdevice (e.g., the electrical signals that are detected at each lead) maybe determined based on the corresponding simulated three-dimensionalrepresentation of the subject's internal anatomy to generate, forexample, a simulated electrocardiogram, a simulatedelectroencephalogram, and/or the like. For instance, the spread of anelectric potential across the subject's heart as well as thecorresponding signals that may be detected on the surface of thesubject's skin may be simulated based at least on the subject'sanatomical attributes (e.g., skeletal properties, organ geometry,musculature, subcutaneous fat distribution, and/or the like) indicatedby the simulated three-dimensional representation corresponding to thesubject's internal anatomy.

Determining the relationship between the target organ's simulatedelectrical activity and the simulated body surface electrode readings, asubject-specific transformation matrix that accounts for variations inlead placement and subject anatomy may be computed. Thissubject-specific transformation matrix, or correction matrix, may beused to more accurately determine the precise electrical activationpattern and orientation of the organ. For example, the subject-specifictransformation matrix may be applied to generate a corrected electrogramand/or a corrected vectorgram (e.g. a corrected electrocardiogram, acorrected electroencephalogram, a corrected vectorcardiogram, and/or thelike). The corrected electrogram may lead to improved diagnostic outputand improved mapping of the source of the cardiac arrhythmia.

FIG. 9A depicts a flowchart illustrating an example of an imagingprocess 900, in accordance with some example embodiments. Referring toFIGS. 1 and 9A, the process 900 may be performed by the simulationcontroller 110. For example, the simulation controller 110 may performthe imaging process 900 in order to generate a three-dimensionalrepresentation of an internal anatomy of the subject 210 by at leastidentifying a simulated three-dimensional representation in the imagelibrary 135 that corresponds to the internal anatomy of the subject 210.Alternatively and/or additionally, the imaging process 900 may beperformed to determine, based on the simulated three-dimensionalrepresentation corresponding to the internal anatomy of the subject 210,a diagnosis for the subject 210. Furthermore, in some exampleembodiments, the simulation controller 100 may perform the imagingprocess 900 in order to simulate the electrical activities of one ormore organs of the subject 210.

At 902, the simulation controller 110 may generate an image libraryincluding a plurality of simulated three-dimensional representations ofinternal anatomies that are each associated with a diagnosis and one ormore computed two-dimensional images. For example, as shown in FIG. 2 ,the image library 135 may include a plurality of simulatedthree-dimensional representations including, for example, the firstsimulated three-dimensional representation 220 a, the second simulatedthree-dimensional representation 220 b, the third simulatedthree-dimensional representation 220 c, and/or the like. The firstsimulated three-dimensional representation 220 a, the second simulatedthree-dimensional representation 220 b, and/or the third simulatedthree-dimensional representation 220 c may also depict a variety ofdifferent anatomical attributes. For instance, the first simulatedthree-dimensional representation 220 a, the second simulatedthree-dimensional representation 220 b, and/or the third simulatedthree-dimensional representation 220 c may be existing three-dimensionalrepresentations of the internal anatomies of one or more referencesubjects exhibiting a variety of different anatomical attributesincluding, for example, variations in skeletal properties (e.g., size,abnormalities, and/or the like), organ geometry (e.g., size, relativeposition, and/or the like), musculature, subcutaneous fat distribution,and/or the like. Alternatively and/or additionally, one or moreanatomical variations may be introduced computationally into the firstsimulated three-dimensional representation 220 a, the second simulatedthree-dimensional representation 220 b, and/or the third simulatedthree-dimensional representation 220 c.

In some example embodiments, the simulated three-dimensionalrepresentations included in the image library 135 may be used togenerate the computed two-dimensional images included in the imagelibrary 135. For example, referring again to FIG. 2 , the first computedtwo-dimensional image 225 a may be generated based on the firstsimulated three-dimensional representation 220 a, the second computedtwo-dimensional image 225 b may be generated based on the secondsimulated three-dimensional representation 220 b, and the third computedtwo-dimensional image 225 c may be generated based on the thirdsimulated three-dimensional representation 220 c.

The first computed two-dimensional image 225 a, the second computedtwo-dimensional image 225 b, and the third computed two-dimensionalimage 225 c may each be generated by exposing, to a simulated radiationsource, the corresponding first simulated three-dimensionalrepresentation 220 a, the second simulated three-dimensionalrepresentation 220 b, and the third simulated three-dimensionalrepresentation 220 c. For instance, the first computed two-dimensionalimage 225 a may be generated by at least determining, based at least ona density and/or transmissivity of the different tissues included in thefirst simulated three-dimensional representation 220 a, a quantity ofradiation (e.g., from a simulated radiation source) that is able to passthrough the different tissues included in the first simulatedthree-dimensional representation 220 a to form the first computedtwo-dimensional image 225 a. Alternatively and/or additionally, thesecond computed two-dimensional image 225 b may be generated by at leastdetermining, based at least on a density and/or transmissivity of thedifferent tissues forming each of the anatomical structures (e.g.,organs) included in the second simulated three-dimensionalrepresentation 220 b, a quantity of radiation (e.g., from a simulatedradiation source) that is able to pass through the different tissuesincluded in the second simulated three-dimensional representation 220 bto form the second computed two-dimensional image 225 b.

Furthermore, in some example embodiments, each of the simulatedthree-dimensional representations and the corresponding computedtwo-dimensional images included in the image library 135 may beassociated with a primary symptom or complaint as well as a diagnosis.For example, the first computed two-dimensional image 225 a, the secondcomputed two-dimensional image 225 b, and the third computedtwo-dimensional image 225 c may be associated with the complaint orsymptom of “chest discomfort.” Moreover, the first simulatedthree-dimensional representation 220 a (and the first computedtwo-dimensional image 225 a) may be associated with a diagnosis ofdilated cardiomyopathy, the second simulated three-dimensionalrepresentation 220 b (and the second computed two-dimensional image 225b) may be associated with a diagnosis of a pulmonary embolism, and thethird simulated three-dimensional representation 220 c (and the thirdcomputed two-dimensional image 225 c) may be associated with a diagnosisof a rib fracture.

At 904, the simulation controller 110 may identify, in the imagelibrary, a simulated three-dimensional representation corresponding toan internal anatomy of a subject based at least on a match between acomputed two-dimensional image corresponding to the simulatedthree-dimensional representation and a two-dimensional image of theinternal anatomy of the subject. For example, the simulation controller110 may apply one or more image comparison techniques in order todetermine whether the two-dimensional image 215 matches the firstcomputed two-dimensional image 225 a associated with the first simulatedthree-dimensional representation 220 a, the second computedtwo-dimensional image 225 b associated with the second simulatedthree-dimensional representation 220 b, and/or the third computedtwo-dimensional image 225 c associated with the third simulatedthree-dimensional representation 220 c. The one or more image comparisontechniques may include scale invariant feature transform (SIFT), speedup robust feature (SURF), binary robust independent elementary features(BRIEF), oriented FAST and rotated BRIEF (ORB), and/or the like.Alternatively and/or additionally, the one or more image comparisontechniques may include one or more machine learning models trained toidentify similar images including, for example, autoencoders, neuralnetworks, and/or the like.

In some example embodiments, the match between the two-dimensional image215 and one or more of the first computed two-dimensional image 225 a,the second computed two-dimensional image 225 b, and the third computedtwo-dimensional image 225 c may be probabilistic. For example, as shownin FIG. 2 , the simulation controller 110 may determine that the firstcomputed two-dimensional image 225 a is 75% similar to thetwo-dimensional image 215, the second computed two-dimensional image 225b is 5% similar to the two-dimensional image 215, and the third computedtwo-dimensional image 225 c is 55% similar to the two-dimensional image215. The simulation controller 110 may determine, based at least on acomputed two-dimensional image having a highest similarity index and/ora similarity index exceeding a threshold value, that one or more of thefirst computed two-dimensional image 225 a, the second computedtwo-dimensional image 225 b, and the third computed two-dimensionalimage 225 c match the two-dimensional image 215.

In some example embodiments, the time and computation resourcesassociated with searching the image library 135 for one or more computedtwo-dimensional images matching the two-dimensional image 215 may bereduced by applying one or more filters to eliminate at least some ofthe computed two-dimensional images from the search. For example, thecomputed two-dimensional images (and the corresponding simulatedthree-dimensional representations) included in the image library 135 maybe indexed based on one or more attributes such as, for example, thedemographics (e.g., age, gender, and/or the like) and/or the vitalstatistics (e.g., height, weight, and/or the like) of reference subjectsdepicted in the computed two-dimensional image. Alternatively and/oradditionally, the computed two-dimensional images (and the correspondingsimulated three-dimensional representations) included in the imagelibrary 135 may be indexed based on the corresponding diagnosis and/ortypes of diagnosis.

Accordingly, instead of comparing the two-dimensional image 215 to everycomputed two-dimensional image included in the image library 135, thesimulation controller 110 may eliminate, based on the demographics, thevital statistics, and/or the symptoms of the subject 210, one or morecomputed two-dimensional images of reference subjects having differentdemographics, different vital statistics, and/or diagnosis that areinconsistent with the symptoms of the subject 210. For example, if thesubject 210 exhibits symptoms consistent with a heart condition, theimage library 315 may exclude, from the search of the image library 135,the third computed two-dimensional image 225 c based at least on thethird computed two-dimensional image 225 c being associated with adiagnosis (e.g., rib fracture) that is inconsistent with the symptoms ofthe subject 210.

At 906, the simulation controller 110 may generate a first outputincluding the simulated three-dimensional representation correspondingto the internal anatomy of the subject and/or a diagnosis associatedwith the simulated three-dimensional representation. For example, inresponse to the two-dimensional image 215 of the subject 210 beingmatched to the first computed two-dimensional image 225 a, thesimulation controller 110 may generate an output including the firstsimulated three-dimensional representation 220 a and/or the diagnosis(e.g., dilated cardiomyopathy) associated with the first simulatedthree-dimensional representation 220 a. The simulation controller 110may generate the output to also include a value indicative of thecloseness of the match (e.g., 75% similar) between the two-dimensionalimage 215 and the first computed two-dimensional image 225 a.Alternatively and/or additionally, the simulation controller 110 maygenerate the output to include a value indicative of a probability ofthe diagnosis associated with the first simulated three-dimensionalrepresentation 220 a (e.g., 75% chance of dilated cardiomyopathy).

It should be appreciated that the simulation controller 110 may send, tothe client 120, the first output including the simulatedthree-dimensional representation corresponding to the internal anatomyof the subject and/or a diagnosis associated with the simulatedthree-dimensional representation. Alternatively and/or additionally, thesimulation controller 110 may generate a user interface configured todisplay, at the client 120, the first output including the simulatedthree-dimensional representation corresponding to the internal anatomyof the subject and/or a diagnosis associated with the simulatedthree-dimensional representation.

At 908, the simulation controller 110 may determine, based at least onone or more clinical two-dimensional images of the subject and thesimulated three-dimensional representation corresponding to the internalanatomy of the subject, a lead placement for a recording devicemeasuring an electrical activity of an organ of the subject. Forexample, the lead placement for electrocardiography (ECG) to measure theelectrical activities of the heart and/or electroencephalography (EEG)to measure the electrical activities of the brain may be determinedbased on the images 610 and 620 corresponding to FIGS. 9C and 9D.

At 910, the simulation controller 110 may generate, based at least onthe lead placement and the simulated three-dimensional representationcorresponding to the internal anatomy of the subject, a second outputincluding the lead placement and a simulation of the electricalactivities measured by the recording device. For example, in someexample embodiments, the simulation controller 110 may furtherdetermine, based at least on the lead placement (e.g., determined atoperation 908) and the first simulated three-dimensional representation220 a corresponding to the internal anatomy of the subject 210, asimulated electrocardiogram (ECG) depicting the electrical activities ofthe heart and/or a simulated electroencephalography (EEG) depicting theelectrical activities of the brain. The simulated electrocardiogram(ECG) and/or the simulated electroencephalography (EEG) may depict thesignals that may be measured by each lead placed in accordance with theplacement determined in operation 908. For instance, a simulatedelectrocardiogram may depict the voltage changes that may be measured byeach lead on the surface of the subject's skin. These voltage changesmay correspond to the electrical activities of the subject's heartincluding, for example, the dipole that is created due to the successivedepolarization and repolarization of the heart.

In some example embodiments, the simulation controller 110 may send, tothe client 120, the second output including the lead placement and/orthe simulation of the electrical activities measured by the recordingdevice. Alternatively and/or additionally, the simulation controller 110may generate a user interface configured to display, at the client 120,the second output including the lead placement and/or the simulation ofthe electrical activities measured by the recording device.

FIG. 9B depicts a flowchart illustrating another example of an imagingprocess 950, in accordance with some example embodiments. Referring toFIGS. 1 and 9B, the process 950 may be performed by the simulationcontroller 110. For example, the simulation controller 110 may performthe imaging process 950 in order to generate a three-dimensionalrepresentation of an internal anatomy of the subject 210 by at leastidentifying a simulated three-dimensional representation in the imagelibrary 135 that corresponds to the internal anatomy of the subject 210.Alternatively and/or additionally, the imaging process 950 may beperformed to determine, based on the simulated three-dimensionalrepresentation corresponding to the internal anatomy of the subject 210,a diagnosis for the subject 210. Furthermore, in some exampleembodiments, the simulation controller 100 may perform the imagingprocess 950 in order to simulate the electrical activities of one ormore organs of the subject 210 to produce a customized simulationenvironment of the subject including the electrical activity of an organand the simulated body surface electrical activity including thesimulated body surface recordings detected by the recording electrodes(bottom right box labelled Product 2).

As shown in FIG. 9B, the simulation controller 110 may receive inputsincluding (1) demographic and clinical information such as age, weight,sex, clinical situation, and symptoms; (2) two-dimensional clinicalimages from one or more views (examples include FIGS. 6A and 6B); and(3) subject electrical recordings (e.g. a clinical electrogram orvectorgram such as, for example, a clinical electrocardiogram,electroencephalogram, vectorcardiogram, and/or the like).

In some example embodiments, the image library 135 may be created fromsubject-derived, three-dimensional representations of subject anatomy.The simulated two-dimensional images may be created to include simulatedtwo-dimensional images from different angles. Moreover, the simulatedtwo-dimensional images and the corresponding three-dimensional modelsmay be indexed with one or more subject attributes including, forexample, weight, height, sex, clinical situation, symptoms, and/or thelike.

For a specific subject, the simulation controller may receive inputsincluding, for example, the subject's age, weight, height, sex, clinicalsituation, and symptoms (FIG. 9B, Input 1). The simulation controller110 may select an appropriate simulation library (FIG. 9B, face symbol)for the intended instance (FIG. 9B, Intermediate Product 1).Furthermore, the simulation controller 110 may receive one or moretwo-dimensional images of the subject's anatomy (FIG. 9B, Input 2) andcompares these two-dimensional images to the computed two-dimensionalimages included in the image library 135. Computed two-dimensionalimages with the highest correlation with the subject's two-dimensionalimages may be identified. A combination of the highest matching computedtwo-dimensional images, the corresponding three-dimensionalrepresentations, and the associated case information (e.g.,demographics, clinical situation, diagnosis, and/or the like) may beoutput by the simulation controller 110 (FIG. 9B, Product 1).

In some example embodiments, the simulation controller 110 may furtheridentify the locations of one or more leads (e.g., pairs of surfaceelectrodes) in the subject's two-dimensional images and calculatespositions of the leads relative to the subject's skin (FIG. 9B,Intermediate Product 2). The simulation controller 110 may compute theangular and spatial relationship between the actual lead placement, thetarget organ (e.g., heart, brain, and/or the like), and the position ofstandard lead placements, thereby creating a subject-specificthree-dimensional simulation environment suitable for simulating theelectrical activities of the target organ (FIG. 9B, Intermediate Product3).

A simulation of the electrical activation of the organ may be performedwithin the subject-specific three-dimensional simulation environmentincluding the three-dimensional representation corresponding to thesubject's internal anatomy. For example, the simulated electrical fieldfrom the organ may be calculated as the electrical field diffusesthrough body tissues to the skin surface. Simulated recordings at boththe subject-specific electrode positions and standard electrodepositions may be computed. The relationship between the organ'selectrical activation and the body surface recordings may be used tocompute correction function for each electrode site (e.g. a“nonstandard-to-standard correction matrix”) and for correcting betweenthe organ's electrical activation pattern and that observed at the bodysurface (e.g. a “vectorgram correction matrix”).

The subject's recorded electrogram is then analyzed. Using thecorrection matrices, a standardized electrogram (e.g. FIG. 9B, Product2) and/or a spatially and rotationally-corrected vectorgram (e.g. FIG.9B, Product 3) may be generated. The standardized electrogram may beused to increase the diagnostic accuracy of the recorded electrogramwhile the corrected vectorgram may be used to increase the accuracy ofan arrhythmia source localization system.

It should be appreciated that the simulation controller 110 may operate(1) to create a simulated three-dimensional representation of asubject's internal anatomy as well as a computational assessment ofdiagnosis probability (FIG. 9B: Potential Use 1); (2) to convert anonstandard electrogram (e.g. nonstandard 12-lead electrocardiogram) toa standard electrogram (e.g. standard 12-lead electrocardiogram) (FIG.9B: Potential Use 2) to improve the diagnostic accuracy of theelectrogram; and (3) to correct for subject-specific variations inelectrode position and subject anatomy in the calculation of athree-dimensional vectorgram (e.g., vectorcardiogram and/or the like) topermit an accurate electrical source mapping (e.g. for use in arrhythmiasource localization) (FIG. 9B, Potential Use 3).

FIG. 9C depicts a block diagram illustrating an example of process 960for generating a corrected electrogram, in accordance with some exampleembodiments. Referring to FIGS. 1 and 9C, the process 960 may beperformed by the simulation controller 110 in order to generate acorrected electrogram that accounts for variations in lead placement andsubject anatomy.

As shown in FIG. 9C, the simulation controller 110 may generate, basedat least on a simulated three-dimensional representation of thesubject's internal anatomy (e.g., thorax cavity and/or the like), arhythm simulation (e.g., ventricular tachycardia and/or the like). Thesimulated three-dimensional representation of the subject's internalanatomy may be identified based on one or more clinical two-dimensionalimages of the subject's internal anatomy. Moreover, a first plurality ofsurface electrode recordings may be computed based on the rhythmsimulation to account for subject-specific lead placements, which maydeviate from standard lead placements. A second plurality of surfaceelectrode recordings corresponding to standard lead placements may alsobe computed based on the rhythm simulation.

In some example embodiments, a transformation matrix A may be generatedbased on a difference between the first plurality of surface electroderecordings and the second plurality of surface electrode recordings. Thetransformation matrix A may capture variations in lead placement as wellas subject anatomy. Accordingly, the transformation matrix A may beapplied to a clinical electrogram (e.g., a clinical electrocardiogram, aclinical electroencephalogram, and/or the like) to generate a correctedelectrogram (e.g., a corrected electrogram, a correctedelectroencephalogram, and/or the like) by at least removing, from theclinical electrogram, deviations that are introduced by non-standardlead placement and/or anatomical variations.

FIG. 9D a block diagram illustrating an example of process 970 forgenerating a corrected vectorgram, in accordance with some exampleembodiments. Referring to FIGS. 1 and 9D, the process 970 may beperformed by the simulation controller 110 in order to generate acorrected electrogram that accounts for variations in lead placement andsubject anatomy.

As shown in FIG. 9D, the simulation controller 110 may generate, basedat least on a simulated three-dimensional representation of thesubject's internal anatomy (e.g., thorax cavity and/or the like), arhythm simulation (e.g., ventricular tachycardia and/or the like). Thesimulated three-dimensional representation of the subject's internalanatomy may be identified based on one or more clinical two-dimensionalimages of the subject's internal anatomy. Further based on the rhythmsimulation, the simulation controller 110 may generate a simulatedthree-dimensional electrical properties of a target organ (e.g., heart,brain, and/or the like) as well as a simulation of body surfaceelectrical potentials and electrical recordings. A simulatedthree-dimensional vectorgram (e.g., a vectorcardiogram and/or the like)may be generated based on the simulated body surface recordings.

In some example embodiments, a transformation matrix A may be generatedbased on a difference between the simulated three-dimensional electricalproperties of the target organ and the simulated body surfacerecordings. The transformation matrix A may capture variations in leadplacement as well as subject anatomy. Accordingly, the transformationmatrix A may be applied to a clinical vectorgram (e.g., a clinicalvectorcardiogram and/or the like) to generate a corrected vectorgram(e.g., a corrected vectorcardiogram and/or the like) by at leastremoving, from the clinical vectorcardiogram, deviations arising fromnon-standard lead placement and/or anatomical variations.

FIG. 10 depicts a block diagram illustrating a computing system 1000, inaccordance with some example embodiments. Referring to FIGS. 1 and 5 ,the computing system 1000 can be used to implement the simulationcontroller 110 and/or any components therein.

As shown in FIG. 10 , the computing system 1000 can include a processor1010, a memory 1020, a storage device 1030, and input/output device1040. The processor 1010, the memory 1020, the storage device 1030, andthe input/output device 1040 can be interconnected via a system bus1050. The processor 1010 is capable of processing instructions forexecution within the computing system 1000. Such executed instructionscan implement one or more components of, for example, the simulationcontroller 110. In some implementations of the current subject matter,the processor 1010 can be a single-threaded processor. Alternately, theprocessor 1010 can be a multi-threaded processor. The processor 1010 iscapable of processing instructions stored in the memory 1020 and/or onthe storage device 1030 to display graphical information for a userinterface provided via the input/output device 1040.

The memory 1020 is a computer readable medium such as volatile ornon-volatile that stores information within the computing system 1000.The memory 1020 can store data structures representing configurationobject databases, for example. The storage device 1030 is capable ofproviding persistent storage for the computing system 1000. The storagedevice 1030 can be a floppy disk device, a hard disk device, an opticaldisk device, or a tape device, or other suitable persistent storagemeans. The input/output device 1040 provides input/output operations forthe computing system 1000. In some implementations of the currentsubject matter, the input/output device 1040 includes a keyboard and/orpointing device. In various implementations, the input/output device1040 includes a display unit for displaying graphical user interfaces.

According to some implementations of the current subject matter, theinput/output device 1040 can provide input/output operations for anetwork device. For example, the input/output device 1040 can includeEthernet ports or other networking ports to communicate with one or morewired and/or wireless networks (e.g., a local area network (LAN), a widearea network (WAN), the Internet).

In some implementations of the current subject matter, the computingsystem 1000 can be used to execute various interactive computer softwareapplications that can be used for organization, analysis and/or storageof data in various (e.g., tabular) format. Alternatively, the computingsystem 1000 can be used to execute any type of software applications.These applications can be used to perform various functionalities, e.g.,planning functionalities (e.g., generating, managing, editing ofspreadsheet documents, word processing documents, and/or any otherobjects, etc.), computing functionalities, communicationsfunctionalities, and/or the like. The applications can include variousadd-in functionalities or can be standalone computing products and/orfunctionalities. Upon activation within the applications, thefunctionalities can be used to generate the user interface provided viathe input/output device 1040. The user interface can be generated andpresented to a user by the computing system 1000 (e.g., on a computerscreen monitor, etc.).

One or more aspects or features of the subject matter described hereincan be realized in digital electronic circuitry, integrated circuitry,specially designed application specific integrated circuits (ASICs),field programmable gate arrays (FPGAs) computer hardware, firmware,software, and/or combinations thereof. These various aspects or featurescan include implementation in one or more computer programs that areexecutable and/or interpretable on a programmable system including atleast one programmable processor, which can be special or generalpurpose, coupled to receive data and instructions from, and to transmitdata and instructions to, a storage system, at least one input device,and at least one output device. The programmable system or computingsystem may include clients and servers. A client and server aregenerally remote from each other and typically interact through acommunication network. The relationship of client and server arises byvirtue of computer programs running on the respective computers andhaving a client-server relationship to each other.

These computer programs, which can also be referred to as programs,software, software applications, applications, components, or code,include machine instructions for a programmable processor, and can beimplemented in a high-level procedural and/or object-orientedprogramming language, and/or in assembly/machine language. As usedherein, the term “machine-readable medium” refers to any computerprogram product, apparatus and/or device, such as for example magneticdiscs, optical disks, memory, and Programmable Logic Devices (PLDs),used to provide machine instructions and/or data to a programmableprocessor, including a machine-readable medium that receives machineinstructions as a machine-readable signal. The term “machine-readablesignal” refers to any signal used to provide machine instructions and/ordata to a programmable processor. The machine-readable medium can storesuch machine instructions non-transitorily, such as for example as woulda non-transient solid-state memory or a magnetic hard drive or anyequivalent storage medium. The machine-readable medium canalternatively, or additionally, store such machine instructions in atransient manner, such as for example, as would a processor cache orother random access memory associated with one or more physicalprocessor cores.

The subject matter described herein can be embodied in systems,apparatus, methods, and/or articles depending on the desiredconfiguration. The implementations set forth in the foregoingdescription do not represent all implementations consistent with thesubject matter described herein. Instead, they are merely some examplesconsistent with aspects related to the described subject matter.Although a few variations have been described in detail above, othermodifications or additions are possible. In particular, further featuresand/or variations can be provided in addition to those set forth herein.For example, the implementations described above can be directed tovarious combinations and subcombinations of the disclosed featuresand/or combinations and subcombinations of several further featuresdisclosed above. In addition, the logic flows depicted in theaccompanying figures and/or described herein do not necessarily requirethe particular order shown, or sequential order, to achieve desirableresults. Other implementations may be within the scope of the followingclaims.

1-58. (canceled)
 59. One or more computing systems for determining datarelating to a subject, the one or more computing systems comprising: oneor more computer-readable storage mediums that store computer-executableinstructions for controlling the one or more computing systems to:access a subject two-dimensional (2D) image of the subject; access alibrary of computed 2D images, each computed 2D image being generatedbased on a simulated three-dimensional (3D) representation of asimulated internal anatomy, each computed 2D image formed by simulatinga quantity of radiation emitted from a simulated radiation source thatpasses through a simulated 3D representation onto a simulated surface,each simulated 3D representation associated with data; identify acomputed 2D image that matches the subject 2D image; and output anindication of the data associated with the simulated 3D representationfrom which the identified computed 2D image was generated; and one ormore processors for controlling the one or more computing systems toexecute one or more of the computer-executable instructions.
 60. The oneor more computing systems of claim 59 wherein the instructions furtherinclude instructions to generate the library by generating a pluralityof simulated 3D representations based on anatomical variations.
 61. Theone or more computing systems of claim 59 wherein the library includesmultiple computed 2D images representing different views of the samesimulated 3D representation.
 62. The one or more computing systems ofclaim 61 wherein different views are based on varying position and/ororientation of the simulated radiation source.
 63. The one or morecomputing systems of claim 59 wherein the quantity of simulatedradiation that passes through a simulated 3D representation factors inradiation transmissivity of anatomical structures represented by asimulated internal anatomy.
 64. The one or more computing systems ofclaim 59 wherein a computed 2D image represents a computed X-ray image.65. The one or more computing systems of claim 59 wherein theinstructions include instructions to output an indication of closenessof the match between the identified computed 2D image and the subject 2Dimage.
 66. The one or more computing systems of claim 59 wherein thedata is a diagnosis.
 67. The one or more computing systems of claim 59wherein the instructions further identify another computed 2D image thatmatches the subject 2D image, identify other data associated with theother computed 2D image, and output another indication of the otherdata.
 68. The one or more computing systems of claim 67 wherein theinstructions further output an indication of closeness of the matchbetween the identified computed 2D image and the subject 2D image andoutput another indication of closeness of the match between the othercomputed 2D image and the subject 2D image.
 69. The one or morecomputing systems of claim 68 wherein the closeness of the matchesindicates a probability associated with the data.
 70. The one or morecomputing systems of claim 59 wherein each of a plurality of computed 2Dimages are associated with simulated attributes relating to thesimulated 3D representation from which the computed 2D image was formedand wherein the computed 2D image is identified computed 2D imagesassociated with simulated attributes that do not include simulatedattributes that are different from subject attributes of the subject.71. The one or more computing systems of claim 59 wherein theinstructions further generate a plurality of 3D representations based oninternal anatomies with computationally introduced anatomical variationsof an existing internal anatomy.
 72. The one or more computing systemsof claim 71 wherein the variations include one or more of variations ofskeletal properties, organ geometries, musculature, and fatdistribution.
 73. The one or more computing systems of claim 71 whereina variation is a skeletal abnormality.
 74. The one or more computingsystems of claim 73 wherein the abnormality is a bone fracture.
 75. Amethod performed by one or more computing systems for determining adiagnosis for a subject, the method comprising: accessing a subjecttwo-dimensional (2D) image of the subject; identifying a computed 2Dimage that matches the subject 2D image, the identified computed 2Dimage being generated based on a simulated three-dimensional (3D)representation of an internal anatomy; identifying a diagnosisassociated with the identified computed 2D image; and outputting anindication of the diagnosis.
 76. The method of claim 75 furthercomprising outputting an indication of closeness of the match betweenthe computed 2D image and the subject 2D image.
 77. The method of claim75 further comprising identifying another computed 2D image that matchesthe subject 2D image, identifying another diagnosis associated with theidentified other computed 2D image, and outputting another indication ofthe other diagnosis.
 78. The method of claim 77 further comprisingoutputting an indication of closeness of the match between the computed2D image and the subject 2D image and outputting another indication ofcloseness of the match between the other computed 2D image and thesubject 2D image.
 79. The method of claim 78 wherein the closeness of amatch indicates a probability of a diagnosis.
 80. The method of claim 75wherein each of a plurality of computed 2D images is associated withsimulated attributes relating to the simulated 3D representation fromwhich that computed 2D image was generated and wherein the computed 2Dimage is identified from computed 2D images that do not include computed2D images associated with simulated attributes that are different fromsubject attributes of the subject.
 81. The method of claim 75 whereineach of a plurality of computed 2D images is associated with a symptomand wherein the computed 2D image is identified from computed 2D imagesassociated a symptom or complaint that matches a subject symptom orsubject complaint of the subject.
 82. The method of claim 75 furthercomprising generating a plurality of simulated 3D representations basedon internal anatomies with computationally introduced anatomicalvariations of an existing internal anatomy.
 83. The method of claim 82wherein the variations include one or more of variations of skeletalproperties, organ geometries, musculature, and fat distribution.
 84. Themethod of claim 83 wherein a variation is a skeletal abnormality. 85.The method of claim 84 wherein the abnormality is a bone fracture. 86.The method of claim 75 wherein the computed 2D image is generated fromthe simulated 3D representation by simulating effects of exposure to aradiation source.
 87. The method of claim 75 wherein the identifying ofa computed 2D image that matches the subject 2D image further comprisesapplying a machine learning model that determines that the subject 2Dimage matches the computed 2D image.
 88. One or more computing systemscomprising: one or more computer-readable storage mediums that storecomputer-executable instructions for controlling the one or morecomputing systems to: for each of a plurality of three-dimensional (3D)representations of an internal anatomy having anatomical structures,generate a plurality of simulated internal anatomies representing avariation of the anatomical structures of that internal anatomy, ananatomical structure associated with radiation transmissivity; for eachof a plurality of simulated internal anatomies, generate a simulated 3Drepresentation of that simulated internal anatomy; and for each of aplurality of positions, generate a computed two-dimensional (2D) imagebased on that simulated internal anatomy by simulating a quantity ofradiation emitted from a simulated radiation source at that positionthat passes through that simulated 3D representation onto a simulatedsurface, wherein the simulating of the quantity of radiation factors inradiation transmissivity of an anatomical structure of the internalanatomy; and store the computed 2D image; and one or more processors forcontrolling the one or more computing systems to execute one or more ofthe computer-executable instructions.
 89. The one or more computingsystems of claim 88 wherein at least some of the 3D representations arebased on an internal anatomy of a reference subject.
 90. The one or morecomputing systems of claim 88 wherein the variations include one or moreof skeletal properties, organ geometries, musculature, and fatdistribution.
 91. The one or more computing systems of claim 88 whereinthe instructions further include instructions to produce a radiographbased on a computed 2D image.
 92. The one or more computing systems ofclaim 88 wherein the instructions further control the one or morecomputing systems to associate a diagnosis with a computed 2D image.